SOCW 6210

Genetic and Environmental Influences on Female Sexual Orientation, Childhood Gender Typicality and Adult Gender Identity Andrea Burri1,2*, Lynn Cherkas2, Timothy Spector2, Qazi Rahman1*

1 Biological and Experimental Psychology Group, School of Biological and Chemical Sciences, Queen Mary University of London, London, United Kingdom, 2 Department

of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom

Abstract

Background: Human sexual orientation is influenced by genetic and non-shared environmental factors as are two important psychological correlates – childhood gender typicality (CGT) and adult gender identity (AGI). However, researchers have been unable to resolve the genetic and non-genetic components that contribute to the covariation between these traits, particularly in women.

Methodology/Principal Findings: Here we performed a multivariate genetic analysis in a large sample of British female twins (N = 4,426) who completed a questionnaire assessing sexual attraction, CGT and AGI. Univariate genetic models indicated modest genetic influences on sexual attraction (25%), AGI (11%) and CGT (31%). For the multivariate analyses, a common pathway model best fitted the data.

Conclusions/Significance: This indicated that a single latent variable influenced by a genetic component and common non- shared environmental component explained the association between the three traits but there was substantial measurement error. These findings highlight common developmental factors affecting differences in sexual orientation.

Citation: Burri A, Cherkas L, Spector T, Rahman Q (2011) Genetic and Environmental Influences on Female Sexual Orientation, Childhood Gender Typicality and Adult Gender Identity. PLoS ONE 6(7): e21982. doi:10.1371/journal.pone.0021982

Editor: Stacey Cherny, University of Hong Kong, Hong Kong

Received March 9, 2011; Accepted June 14, 2011; Published July 7, 2011

Copyright: � 2011 Burri et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: These authors have no support or funding to report.

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: [email protected] (AB); [email protected] (QR)

Introduction

Understanding of the origins of sexual orientation can help

narrow competing developmental explanations for behavioral sex

differences in general and is of increasing importance to

researchers concerned with the physical and mental health of

sexual minorities [1,2]. Homosexuality appears to be a stable

sexual phenotype in humans with population-based surveys

suggesting lifetime prevalence of 2–4% in men and 0.5–1.5% in

women when measured as exclusive same-sex ‘‘feelings’’ (e.g.,

homosexual attractions and fantasies) [3,4]. The distribution of the

trait is generally bimodal and this is stronger for men than it is for

women; a first indication of different, albeit overlapping,

developmental pathways towards male versus female sexual

orientation [5,6]. Basic biobehavioral research into female sexual

orientation appears infrequent compared to that performed on

males.

Several early family and twin studies provide evidence for a

genetic component to both male and female sexual orientation

[7,8,9]. Heritability estimates were found to be in the region of

40% to 50%. However, the putative inheritance patterns have

remained unclear. Family pedigree studies in men have suggested

that maternally inherited factors might be involved [10,11,12,13]

although one large study in a carefully ascertained pedigree has

failed to replicate this [14]. Among females, autosomal and sex-

linked routes have been implicated although there has only ever

been one survey study performed here [15]. Two preliminary

linkage studies reported microsatellite marker loci for male

homosexuality on the X chromosome [10,11] with one confirming

linkage for males but not females [11]. However, two independent

reports found no such linkage in males [16,17]. The latest genome-

wide scan reported several new autosomal markers for male sexual

orientation [18] which again require replication.

Early criticisms of previous studies focused on the possibility that

their reliance on self-selected volunteers (e.g., through advertise-

ments in gay and lesbian press) may have biased the results by

increasing twin resemblance. But it is not clear how this would

inflate concordance rates or overestimate genetic or non-genetic

effects (similarity would be increased for both MZ and DZ twins).

However, two studies of attraction components of sexual

orientation, and one of same-sex sexual behavior, were popula-

tion-based and all reported lower concordance rates than

previously found at around 30% - although Bailey et al. 2000

were unable to resolve genetic, shared and non-shared environ-

mental factors in their univariate models [19,20,21]. One of these

reports supported the notion that developmental pathways

towards homosexuality might be different for men and women

[19]. One further study which modeled several components of

sexual orientation (attractions, attitudes to homosexual sex, and

lifetime same-sex partners) reported stronger evidence for genetic

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influences of between 50% and 60% in females and approximately

30% in males [22]. Despite the inconsistency of findings across

these studies in terms of the magnitude of the heritability estimates,

all of them suggest a genetic component to sexual orientation.

Sexual orientation, like many complex behaviors, comes as a

‘‘package’’ of covarying traits. Critical among these are childhood

gender typicality (or CGT), which are sex-typed behaviors,

activities and interests that are statistically atypical for biological

sex during childhood) and gender identity (psychological gender as

‘‘masculine’’ or ‘‘feminine’’ during adulthood). CGT is robustly

correlated with adult homosexuality as demonstrated in prospec-

tive and retrospective studies and has been observed cross-

culturally [9,23,24,25]. In order to control for possible retrospec-

tive memory biases (based on the argument that homosexuals

might exaggerate nontypicality and heterosexuals understate it)

one study has confirmed the association between CGT and

homosexuality using home videos of childhood behavior [26].

Adult gender identity (AGI), although not a psychometric

homologue to CGT, also shows an association with sexual

orientation as measured via ratings of self-ascribed masculine or

feminine feelings, traditional personality measures of gender (e.g.,

the Bem Sex Role Inventory) and occupational interests [27,28].

Twin models show that both CGT and AGI are heritable

although the estimates vary. Knafo et al. [29] reported

heritability estimates of 37% in boys and almost 80% in girls

(3–4 year olds). Iervolino et al. [30] examined the full range of

normal variation in CGT in the same dataset and found 34%

heritability in boys compared to 57% in girls. Van Beijsterveldt

et al. [31] reported 70% for CGT in both sexes among 7 and 10-

year old twins. Bailey et al. [19] reported heritability of 50% for

men and 37% for women in retrospectively recalled CGT. A

similar wide range of estimates applies to twin models of gender

identity. Lippa and Hershberger [32] reported modest heritability

for their measure based on occupational interests at 53% (no sex

differences) whereas Bailey et al. [19] reported estimates of 31%

for men and 24% for women. Finally, Bailey et al. [19] reported

that the covariation between sexual orientation, CGT and AGI

could be explained by a common familial factor although the

model was a poor statistical fit.

Taken together the data suggest that CGT (and to a lesser

extent, AGI) might be considered as a possible ‘‘sex atypicality’’

endophenotype for trait sexual orientation which could be more

powerful in future gene discovery. Trait sexual orientation is

notoriously skewed. Thus, a broad research strategy which

includes CGT and AGI with their more favorable statistical

distributions will enhance our ability to resolve the molecular

genetics of sexuality.

A related conceptual issue concerns the putative etiological

factor(s) which explain the link between CGT, AGI and sexual

orientation and thus may constitute the ‘‘sex atypicality’’

endophenotype. A good candidate is prenatal sexual differentia-

tion under the action of androgens [1,33]. Homosexuals are

viewed as having been subject to atypical levels of prenatal

androgens thus causing sex-atypical differentiation of brain

structures that control direction of sexual preference, gender-

related psychological traits (including CGT and AGI) and related

traits (such as specific cognitive differences). However, there are

very few direct tests of this hormonal link between measures of sex-

atypicality and trait sexual orientation. There are also few tests of

the developmental progression of this process. For example, do

genetic variations contribute to atypical hormonal levels which

shape CGT and then does CGT precedes the development of trait

sexual orientation? Or are CGT and sexual orientation simply

correlated but develop independently? Strong evidence for an

association between prenatal androgen levels, CGT and sexual

orientation comes from studies of women with congenital adrenal

hyperplasia (CAH) who have been exposed to high levels of

prenatal adrenal androgens. Girls (and adolescents) with CAH

show sex-typed behavior and interests in the male-typical

direction, in spite of strong sex-typical parental gender socializa-

tion [34,35,36]. Adult females with CAH also report significantly

more bisexual/homosexual fantasies and attractions relative to

their control sisters [37,38,39]. Prospective studies in non-clinical

populations also suggest that variations in fetal levels of

testosterone (measured via amniotic sampling) are associated with

male-typical gender related behaviors in girls [40,41]. Finally, a

meta-analysis of the relationship between sexual orientation and

the ratio of the 2 nd

to 4 th

finger digits (a somatic marker ascribed to

the prenatal actions of androgen exposure) revealed a significant

association between male-typical digit ratios and sexual orientation

in women [42]. These lines of evidence support the notion of a

developmental coupling between levels of prenatal androgen,

gender-related behaviors and interests, and sexual orientation

among women.

In the present study, we analyzed questionnaire data from a

large volunteer register of female twins in the United Kingdom to

test the hypotheses that (1) genetic factors significantly influence

variation in measures of sexual orientation and it’s two covariates

– CGT and AGI; (2) that these three traits correlate significantly at

the phenotypic level; and (3) that the covariation among the three

traits is also due to a genetic correlation. This is the first study of its

kind in a British sample.

Methods

Ethic statement The study was approved by the St. Thomas’ Hospital research

ethics committee. All study participants involved in this study

provided informed written consent.

Participants and questionnaire Subjects were monozygotic (MZ) and dizygotic (DZ) volunteer

female twins drawn from the ‘‘TwinsUK’’ registry [43]. Due to

unavailability of data, no males were included in this study.

Zygosity was established using standardized questions about

physical similarity and confirmed by multiplex DNA genotyping

in cases of uncertainty [44].

In 2002, twins were sent a questionnaire asking about general

sexual behavior and sexual orientation (referring to ‘‘sexual

attractions’’ with men and women in this study). Of the 8,418

questionnaires sent, 4,725 (56.1%) were returned. In a 2005

follow-up survey, an anonymous questionnaire assessing CGT and

AGI was also sent to 6,934 female twins in the registry and

returned by 4,850 (69.9%). The questionnaires were developed

previously based on scales in the published literature but shortened

for the purposes of practicality within a large twin register. Twins

were not selected on the basis of variables being studied and were

unaware of any hypothesis being tested.

Final questionnaire data relating to sexual orientation and its

psychological correlates, CGT and AGI, was available on a total of

4,426 female twin individuals - a 49% response rate. Females who

reported never having felt sexually attracted to anyone else

(N = 44; 0.99%) and/or reported never having had sexual

experiences (N = 51; 1.15%) were excluded from the analyses as

were 228 (5.15%) females with missing values for any items

assessing CGT and AGI. Also, 32 (0.72%) women were excluded

because of unknown zygosity. After applying exclusion criteria, a

total of 4,066 women were eligible for analysis, comprising 906

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complete MZ pairs, 806 complete DZ pairs and 642 women

whose co-twins did not participate (15.35%). However, sample

sizes varied somewhat in the different analyses because of missing

scale data.

Demographic information on all twins including age, marital

status, and years of education were obtained from the TwinsUK

database.

Measures Childhood gender typicality (CGT) and adult gender

identity (AGI). The CGT scale consisted of four items

retrospectively assessing childhood sex-typed behavior and

gender identity which are comparable to several published scales

[19,45]. Example items include ‘‘As a child I was called a ‘tomboy’

by my peers’’ and ‘‘As a child I preferred playing with boys rather

than girls’’. Assessment of participants’ self-concepts as masculine

or feminine (AGI) was computed using four items comparable to

those used by Bailey et al. [19]. Example items include ‘‘I don’t

feel very masculine’’ and ‘‘I pride myself on being feminine’’.

Scores for CGT and AGI were derived by adding the point values

for each of the four scale-specific items together and dividing it by

the number of scale items. Response options were on a 7-point

Likert-type scale, ranging from ‘‘strongly agree’’ (1) to ‘‘strongly

disagree’’ (7). Cronbach’s alpha, a measure of internal consistency,

was 0.62 for CGT and 0.44 for AGI. High scores on each measure

mean more feminine.

Sexual orientation. Sexual orientation was measured using a

scale, similar in kind to Kinsey-type scales used extensively in

sexuality research assessing sexual attraction (degree of attraction

towards the same or opposite sex. Response options for this

measure ranged from 1 (‘‘only to/with males, never to/with

females’’) to 5 (‘‘only to/with females, never to/with males’’) with a

supplementary option of ‘‘no sexual attraction’’ (numbered 6)

[19,46].

Analysis For descriptive and genetic analysis CGT, AGI and sexual

attraction were treated as continuous traits based on women’s

responses to the specific questions. Unpaired t-tests (two-tailed)

were used to examine differences between MZ and DZ twins on

age, years of education, CGT, AGI and mean sexual orientation

scores. Two-sample tests of proportions were used to test for

differences in marital status and SES. Pearson’s correlation

coefficients were used to explore patterns of association between

CGT, AGI and sexual orientation scores.

For all analyses, a P value less than 0.05 or odds ratios with a

95% confidence interval not including ‘‘1’’ were considered

statistically significant, unless stated otherwise. Data handling and

descriptive analyses were undertaken using STATA (Intercooled

Stata for Windows 95, Version 5.0, 1997, StataCorp, College

Station, TX) while all genetic modelling was carried out with Mx

software [47].

Univariate genetic modelling. The present study used the

classical twin design where population variance in phenotypes, as

well as covariance between them, can be dissected into genetic and

environmental sources. The twin design assumes that MZ twins

share 100% of both their genes and shared environment, whereas

DZ twins share - on average - 50% of their genes and 100% of

shared environment. Presuming that both types of twins share

equally similar family environments, any greater similarity

between MZ as compared with DZ twin pairs is attributed to

genetic factors.

In the present study maximum likelihood genetic modeling was

used to model latent genetic and environmental factors influencing

sibling covariance in CGT, AGI and sexual orientation for MZ

and DZ twins. Bivariate normality was given for the measures of

CGT and AGI after the variables were transformed. However, for

trait such as sexual orientation, normality cannot be achieved.

Genetic model fitting was used to decompose the observed

phenotypic variance (P) into additive (A) and dominant (D) genetic

effects, and shared (C) and non-shared environmental (E) effects

[48]. The shared environmental variance refers to factors shared

between twin pairs such as family environment. The non-shared

environmental variance reflects factors affecting each twin

individually (e.g., specific prenatal events or peer socialization)

and also includes measurement error.

For continuous phenotypes, evidence for a genetic contribution

(heritability or h 2 ) can be obtained by comparing similarities in

scores using intra-class correlation coefficients (ICCs) for MZ and

DZ twin pairs. Depending on the correlations between the MZ

and DZ twins, either an ACE or an ADE model is fitted. For

univariate models in the present study, an ACE model was applied

when DZ correlations were more than half the MZ correlations.

When the DZ correlations were less than half the MZ correlations,

both ACE and ADE were estimated for comparative purposes

[49]. Initial assessment of the components (A, D, C, and E) may

suggest non-significant values in one or more component. Further

analysis can determine the significance of each factor as

components of the observed variance by removing each

sequentially from the full model and testing the deterioration in

fit of the various nested models, using the likelihood ratio test. In

the present study, the fit of the different models was compared by

taking the fit function and the degrees of freedom (df) of the full

model and subtracting it from the fit function and the df of the

nested restricted models. The subtraction gives an x2 value and associated df that can be tested for significance. In addition, the

Akaike Information Criteria (AIC = x2-2df) was considered, with lower values indicating the most suitable model. The most

parsimonious model was then used to estimate the heritability.

Note the assumption that trait-related environments are similar to

the same degree in MZ and DZ pairs is valid for trait sexual

orientation (Bailey et al., 2000; Kendler et al., 2000). More

detailed descriptions of twin modeling analyses can be found in

Posthuma et al. [50].

Multivariate genetic modelling. Using cross-twin and

cross-trait correlations allows us to partition the covariance

between traits into genetic and environmental components and

therefore permit the quantification of any overlap in the genetic or

environmental correlation between traits. Here we present both

the estimated genetic covariance between the traits as a proportion

of the total phenotypic covariance (bivariate heritability) and the

proportion of the total genetic variance for the traits (genetic

correlation).To test our hypothesis that the covariation among

CGT, AGI, sexual orientation (attraction) can be explained by

genetic correlation between the traits, we further fitted the

following three multivariate models to the data [48,51]: (1) The

Cholesky decomposition provides the correlations between the

three independent genetic and environmental factors (A, C, D, E)

and decomposes the variance for a trait into additive and non-

additive genetic and non-shared environmental effects, providing

the fullest potential explanation of the data. (2) The independent

pathway model is a submodel of the Cholesky model and tests

whether covariance between the traits is explained by a single

underlying genetic factor and a single underlying environmental

factor. (3) The common pathway model assumes that a single

shared latent factor underlies all three measures.

The suitability of the multivariate models was determined by

comparing the models AIC, BIC (Bayesian Information Criterion)

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and their goodness of fit as measured with the likelihood ratio chi-

square test (22LL).

Results

Descriptive analysis Table 1 displays the participant characteristics for the whole

sample and by zygosity group. The MZ and DZ twin groups were

well matched for sexual orientation, CGT, AGI and most

demographic variables except for marital status where MZ twins

were significantly more often married compared with DZ twins

(44.32% vs. 39.10%; P,0.01). Also DZ twins, more than MZ

twins, reported being in a relationship (35.82% vs. 39.85%;

P,0.05) or being widowed (5.58% vs. 7.36%; P,0.01).

Most women displayed ‘‘average’’ AGI scores with the peak

score being at 4.25 (30.9% of total sample). A small fraction of

women had values at both extreme ends of the distribution.

Overall, AGI showed less variability compared with CGT.

Whilst only a negligible proportion of subjects reported a high

degree of childhood gender nonconforming behavior, most of

the women scored in the upper third of the distribution, with

peak scores at 5.5 and 7. We also observed the previously

documented general tendency for women to show more non-

heterosexuality at the predominantly heterosexual end of the

two scales (see Table 2).

Twin similarity Intra-class correlations for MZ and DZ twins in the three

measures are reported in Table 3. For all measures MZ twin

correlations were consistently higher compared with DZ twin

correlations, indicating a genetic contribution to the variance in

these traits. However, the correlations were also all modest

indicating a substantial influence of non-shared environmental

factors. In the case of AGI the correlations were very low

militating against a genetic contribution. For all traits, except

AGI, DZ correlations were less than half the MZ correlations

pointing to the involvement of dominant genetic effects

(Table 3).

Univariate model fitting Based on the intra-class correlations, ACE and ADE models

were fitted for all phenotypes. For all measures the best-fitting

model was an AE model (Table 3). The highest heritability was

found for CGT (32%). Heritability was moderate for sexual

attraction (25%) and small for AGI (11%). There was no effect of

dominance on any of the measures. There was a larger

contribution of non-shared environmental factors to AGI than to

CGT. In contrast to previous studies which produced relatively

large confidence intervals [19,20,21], our confidence intervals

were relatively narrow in comparison (see Table 3) although

probably larger than other twin studies of psychological individual

differences traits (such as personality). Nevertheless, they suggest

some stability of our point estimates.

Multivariate model fitting Genetic and environmental correlations derived from the ADE

Cholesky model are shown in Table 4, along with the phenotypic

correlations. We found significant associations between all three

measures; hence, all measures were included in the multivariate

analyses. The significant correlations ranged from r = 20.21 to

r = 0.05, with the highest correlation being between CGT and

sexual attraction and the lowest between AGI and sexual

attraction (Table 4). The Cholesky results indicated that a

considerable degree of genetic correlation exists especially between

sexual attraction and CGT and AGI (r = 20.42 and r = 20.45,

respectively). The bivariate heritability suggested that approxi-

mately 57% of the covariance between CGT and sexual attraction

is due to additive genetic factors with the remaining 43%

attributable to unique environmental effects (Table 4).

When comparing the independent and common pathway

models with the Cholesky model, the common pathway model

was found to offer the most suitable explanation of the data with

the lowest value of AIC at 3871.1 and the lowest BIC at 222609.7

(Table 5). This common pathway model explains the variance in

each variable in terms of unique A, D and E contributions as well

as a contribution from the ‘‘common sexual orientation pheno-

type’’ (Pc). The parameter estimates derived from the common

Table 1. Means (and standard deviations) for continuous demographic variables, CGT, AGI and sexual orientation (attraction), along with frequency data for discrete demographics for the whole sample and by zygosity group.

Overall (N = 4,066) MZ (N = 2,098) DZ (N = 1,998) P-value*

Mean (SD) Range Mean (SD) Range Mean (SD) Range

Age 53.36 (12.65) 16–87 53.10 (13.43) 16–87 53.66 (11.73) 16–81 0.11

Education in years 10.40 (2.91) 3–33 10.50 (2.93) 6–33 10.35 (2.88) 3–32 0.09

CGT 5.22 (1.25) 1–7 5.25 (1.25) 1–7 5.19 (1.26) 1–7 0.12

AGI 4.39 (0.89) 1–7 4.38 (0.89) 1–7 4.41 (0.91) 1–7 0.28

Sexual attraction 1.13 (0.46) 1–5 1.12 (0.41) 1–5 1.14 (0.50) 1–5 0.16

N % N % N % P-value**

Marital status

Single 209 7.29 96 6.86 113 7.70 0.30

Married 1,194 41.65 620 44.32 574 39.10 0.00

In relationship 1086 37.88 501 35.82 585 39.85 0.01

Divorced 212 7.39 104 7.43 108 5.99 0.07

Widowed 166 5.79 78 5.58 88 7.36 0.02

*Unpaired two-tailed t-test and Mann-Whitney U-tests were used to test for mean differences in response frequencies. **Two-sample test of proportions were used to explore differences in response frequencies. doi:10.1371/journal.pone.0021982.t001

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pathway model are shown in Figure 1. To obtain the contribution

that Pc and unique A, D and E make to the variance in a trait,

squares of the path coefficient are taken. Thus, the model

postulates the existence of an underlying sexual orientation

phenotype (Pc) with a heritability 24% (0.49 2 ) that chiefly explains

the co-occurrence of CGT, AGI and sexual attraction. For CGT

Pc accounts for 43% (0.65 2 ) of the variation, for AGI it accounts

for 4% (0.19 2 ) and for sexual attraction it accounts for 11%

(20.32 2 ) of the variation. The heritability of the variation in the

phenotype that is not accounted for by Pc is 10% (20.32 2 ) for

CGT, 10% (0.32 2 ) for AGI and 19% (0.44

2 ) for sexual attraction.

No influence of D on the variation of Pc and the phenotypes could

be detected. Overall, these results suggest that the common sexual

orientation phenotype does not account for significant variation in

AGI in this model.

Discussion

Our results show that sexual attraction and CGT are influenced

by genetic factors (accounting for 25% and 32% of the variance

respectively). Genetic contributions as estimated in the univariate

analyses had a much weaker impact on AGI (11%). The effect of

non-shared environmental factors (including measurement error)

on all traits was large. However, there was no effect of the shared

family environment on any trait.

These findings are broadly consistent with previous population-

level twin studies demonstrating a heritable basis to male and

female sexual orientation. The heritability estimates reported here

for female sexual attractions were larger than those reported by

Bailey et al. [19] for sexual attraction components (8%). For

attraction, we found no effect of the shared environment in

contrast to Bailey et al. [19] who reported an estimate of 41%.

Langstrom et al. [21] reported shared environmental effects of

same-sex sexual behavior of 17%. Kendler et al. [20] did not

separate their analysis by sex so we cannot compare the findings.

Finally, our genetic estimates were lower than those reported by

Kirk et al. [22] who attempted to model two components of sexual

orientation - sexual attraction and sexual experience - and

reported estimates for females between 50 and 60%. Whilst Kirk

et al. [22] did use attractions in their study they supplemented

these with the measures ‘‘attitudes to homosexual sex’’ and

‘‘lifetime same-sex partners’’ (from a range in an extensive sexual

orientation questionnaire) which are not directly comparable to

measure used here.

The effect of E on all traits was large. E includes phenotypic

variation accounted for by non-shared environment and

measurement error. A variety of sources can cause measure-

ment error, including inadequate or imprecise assessment

instruments and phenotype description, and a variety of

response styles, specifically acquiescence, disacquiescence,

extreme response, midpoint responding, and noncontingent

responding [52]. The twin modelling approach used in this

study does not allow separation of the two sources, hence,

quantification of the influence of measurement error is

impossible. It is therefore likely that some inconsistency

between our heritability estimates for sexual attraction com-

pared to previous work might be due to different usage of

Kinsey-type scales for measuring trait sexual orientation. Here

we used a 5-item measure; Bailey et al. [19] used the full 7-item

Kinsey-scale; Langstrom et al. [21] used number of same-sex

partners; and Kendler et al. [20] employed a single item with

three response choices (heterosexual, bisexual, and homosexual

in attractions). If we compare our data to Bailey et al. (both

studies focused on attractions and using what approximate

traditional Kinsey-type scales), it is possible that the relatively

small difference in response options between the two studies

(that is, a difference of 2) contributed to the differing heritability

estimates for sexual attraction. Parameter estimates for sexual

orientation might be unusually sensitive to the range of items

used to assess the trait and thus future researchers should be

mindful of the utilizing psychometrically robust scales.

Table 2. Percentage of women that checked each item of sexual attraction along with means (and standard deviations) for their respective CGT and AGI scores.

Measure: ‘‘I have felt sexually attracted ’’…’’ % Sexual attraction CGT mean score (SD) AGI mean score (SD)

1 Only to/with males, never to/with females 89.92 5.98 (8.01) 4.43 (0.88)

2 More to/with males than females 8.56 5.44 (8.74) 4.22 (0.91)

3 Equally to/with males and females 0.29 3.90 (1.57) 4.53 (0.55)

4 More to/with females than males 0.86 3.89 (1.33) 4.33 (0.99)

5 Only to/with females, never to/with males 0.36 4.12 (1.41) 4.15 (0.65)

doi:10.1371/journal.pone.0021982.t002

Table 3. Intra-class correlations, cross-twin cross-trait correlations and heritabilities for CGT, AGI and both measures of sexual orientation.

CGT twin 1 AGI twin1 Sexual attraction twin1 Heritability % (95% CI)

CGT twin 2 0.36/0.02 0.03 20.02 0.32 (0.26–0.37)

AGI twin 2 0.03 0.11/0.07 20.02 0.11 (0.05–0.17)

Sexual attraction twin2 20.13 20.06 0.28/0.04 0.25 (0.17–0.33)

Heritability estimates and 95% CIs for all variables are calculated from the best-fitting, most parsimonious univariate AE model. Note. Twin correlations for MZs/DZs are presented on the diagonal. Cross-twin cross-trait correlations for MZs are presented below the diagonal. Cross-twin cross-trait correlations for DZs are presented above the diagonal. doi:10.1371/journal.pone.0021982.t003

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Consistent with several studies the highest heritability was found

for CGT (32%) [19,30]. CGT seems to be the highest heritable

correlate of sexual orientation reported thus far, and furthermore

lies in the region of the h 2

estimates generally reported for sexual

orientation (measured both behaviorally and psychologically). This

adds further support to the notion that CGT may be the main

heritable component or endophenotype of sexual orientation

[53,54]. However, genetic effects for AGI were negligible

compared to previous work and we are less confident about the

validity of AGI as a robust correlate of sexual orientation [19].

The inter-correlations with other measures of sexual orientation

were low for AGI compared to CGT and showed little variability.

Compared with CGT items which capture sex-typed behavior,

interests and identity, AGI comprises identity items only and thus

has restricted psychometric precision. Other documented indices

of gender identity such as ‘‘gender diagnosticity’’ e.g. [27] should

be the focus of future twin studies if suitable short-form scales can be

developed. As with attraction, the inconsistencies between the

studies might be attributed to the number of items in the measures

used. Our measure of CGT comprised four items and Bailey et al. ’s

[19] five items used to check the reliability of self- vs. other-report of

their 24-item measure . The relative comparability here (a

difference between the two studies of only 1 item) provides some

confidence for the validity of heritability estimates reported by both.

However, their measure of AGI comprised seven items and ours

only four (with relatively low internal consistency) which may

explain the differences between studies for this particular measure.

The results from the multivariate analyses presented here

provide evidence of a genetic overlap between CGT and sexual

attraction but less for AGI. We detected a common latent

phenotype with a heritability of 24% underlying sexual orienta-

tion, CGT and AGI as well as moderate phenotype-specific

additive genetic factors and large phenotype-specific non-shared

environmental factor loading on these traits. These data are

supportive of those from Bailey et al. [19] who also found that a

common pathway ACE model best fitted the available data. Both

studies support the notion that showed that genetic and non-

shared environmental factors markedly contributed to the

covariation among the measures, with all three measured variables

(sexual attraction, CGT and AGI) being good indicators of an

underlying latent factor. Overall, the present results support

previous non-twin evidence for the existence of an intermediate

phenotype for sexual orientation, such as for example ‘‘sex-

atypicality’’ [54]. A likely candidate for this latent phenotype is

prenatal androgen exposure which shapes variations in gender

nonconforming behavior and sexual orientation and the develop-

mental coupling between them [1,33,35]. Nevertheless, specula-

tion about origins of this putative sex hormone-related phenotype

is limited by two candidate gene studies of male sexual orientation

both producing null results: one for the androgen receptor [55]

and another for aromatase [56]. However, the absence of such

associations in men does not imply a similar null result in women.

Insofar as our genetic estimates are additive, these data do not

suggest a major role for epistatic or dominant allelic effects. Our

results are also silent on the sources of non-shared environmental

effects. However, what is clear from several twin studies, including

the present, is that shared factors such as the home environment

and parenting styles have little impact on human sexual

orientation. Nevertheless, as we cannot be certain that our

measures, particularly AGI, were robust (given the sizable loading

of a common non-shared environmental factor on each trait) there

is a necessary degree of imprecision to our parameter estimates

and the model fitting results should be treated with caution. Future

studies should be particularly careful in using measures of AGI.

Several other limitations weaken overly strong conclusions from

the present study. The response rate was lower than three other

large twin studies (49%, compared to 53.8% in Bailey et al. [19]

60% in Kendler et al. [20]; and 59.6% in Langstrom et al. [21])

although it is not clear how this could systematically bias the

parameter estimates reported [57]. Also, the response rate here is

in fact comparable to other epidemiological surveys of female

sexual behavior [58,59]. The representativeness of our twin

sample also diminishes any putative selection biases, as shown by a

large comparative study demonstrating that our twin population is

very similar to singletons on a wide range of common health and

lifestyle factors [60]. A comparison of the sample characteristics in

Table 4. Phenotypic, genetic and non-shared environmental correlations among CGT, AGI and sexual orientation (attraction).

CGT-AGI CGT-Sexual attraction AGI-Sexual attraction

rp 0.12 20.21 20.06

proportion of rP due to:

A 0.27 0.57 0.11

D 0.05 0.00 0.00

E 0.68 0.43 0.89

Correlations:

rA 0.21 20.42 20.45

rD - - -

rE 20.11 20.13 0.00

doi:10.1371/journal.pone.0021982.t004

Table 5. Multivariate analysis of three models showing change in model fit (x2) and degrees of freedom (df) when specified parameters are dropped from full ADE model (best fitting models in bold).

Model df AIC BIC 22LL

Cholesky ADE 9008 3875.84 222588.23 21891.84

Independent ADE 9011 3876.25 222596.21 21898.25

Common ADE 9015 3871.08 222609.67 21901.08

AIC = Akaike Information Criterion. AIC describes the model with best goodness-of-fit combined with parsimony. BIC = Bayesian Information Criterion. 22LL = likelihood ratio chi-square test as a measure of goodness of fit. doi:10.1371/journal.pone.0021982.t005

The Genetics of Sexual Orientation

PLoS ONE | www.plosone.org 6 July 2011 | Volume 6 | Issue 7 | e21982

Table 1 show that the MZ and DZ twins did not differ significantly

on most demographic variables, arguing against the tendency for

MZ to be more alike possibly due to shared upbringing. The

comparably low internal consistencies for CGT and AGI may

further reflect the heterogeneous nature of the constructs,

suggesting that more items are needed to capture the range of

manifestations of the constructs. We also used retrospective

measures of CGT which may be influenced by recall biases.

However, prospective studies confirm the predictive psychometric

validity of measures of CGT that are comparable to the one used

here as do studies of maternal reports of proband-recalled CGT

and studies of childhood home videos [23,33].

Due to our considerably large sample size we had enough power

to detect a rather small contribution of non-additive genetic

factors, had it been present (1,800 twin pairs are needed to reject

an AE model with a power of 80% when an ADE model is the true

model, with respective contributions of additive genetics ef-

fects = 0.50, dominant genetic effects = 0.30 and non-shared

environmental effects = 0.20) [48]. Nonetheless, there remained

insufficient numbers of non-heterosexual participants to guarantee

a high degree of statistical power in the genetic and environmental

analyses. This is a well-known problem, as sexual orientation-

related data are notoriously skewed [21].

In summary, we found genetic influences on female sexual

orientation as measured via attractions and on CGT (a key

developmental correlate of sexual orientation). A moderate effect

of a common latent phenotype suggests that there are some

overlapping mechanisms which may be responsible for sexual

orientation. However, stronger conclusions are not warranted at

this stage because of substantial measurement error. Future

research efforts should focus on ‘‘sex-atypicality’’ as a possible

intermediate phenotype for trait sexual orientation which may be

more amenable to gene-mapping approaches.

Acknowledgments

We thank Professor Michael C. Neale (Virginia Commonwealth

University) for helpful discussion regarding the multivariate analysis.

Author Contributions

Conceived and designed the experiments: AVB QR. Performed the

experiments: AVB LC. Analyzed the data: AVB. Wrote the paper: AVB

QR . Internal reviewers: LC TS.

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